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Multi-modal Prompts

Multi-modal prompting combines text with other data types such as images, audio, or code, enabling the language model to process and generate responses based on multiple modalities. This approach is essential for tasks that require understanding or generating information across different formats.

Key Concepts​

  • Multi-modality: Integrating text with images, audio, video, or code in prompts and responses.
  • Cross-modal Reasoning: The model connects information across different data types.
  • Rich Context: Prompts can include visual, auditory, or structured data for deeper understanding.
  • Flexible Output: Responses may include text, code, or references to other modalities.

Best Practices​

  1. Clearly Indicate Modalities

    • Use explicit labels (e.g., [Image], [Audio], [Code]) to distinguish data types.
    • Provide clear instructions on how to interpret or respond to each modality.

  2. Provide Sufficient Context

    • Include relevant details for each modality.
    • Describe images or audio if the model cannot directly process them.

  3. Align Input and Output

    • Specify the expected output format (e.g., text summary of an image, code explanation).
    • Use examples to illustrate cross-modal tasks.

  4. Test for Comprehension

    • Ensure the model can handle the combination of modalities.
    • Adjust prompts if the model struggles with certain data types.

Examples​

Image + Text Example​

[Image: A photo of a dog playing in a park]

Describe what is happening in the image.

Audio + Text Example​

[Audio: Recording of a person saying, "The meeting is at 3 PM."]

Transcribe the audio and summarize the message.

Code + Text Example​

[Code]
def add(a, b):
    return a + b

Explain what this code does in simple terms.

Multi-modal Reasoning Example​

[Image: A bar chart showing sales by month]
[Text: "Sales increased in Q2 due to a new marketing campaign."]

Summarize the key trends and explain the likely cause.

Common Pitfalls​

  1. Ambiguous Modality Markers

    • Not clearly labeling or separating different data types.
    • Confusing the model with mixed or unclear input.

  2. Insufficient Context

    • Providing images or audio without descriptions when the model cannot process them directly.
    • Omitting necessary background information.

  3. Overloading the Prompt

    • Including too many modalities or complex data at once.
    • Making the task too difficult for the model to handle.

  4. Unclear Output Expectations

    • Not specifying the desired response format.
    • Leaving the model to guess how to combine modalities.

Use Cases​

  1. Visual Question Answering

    • Answering questions about images or diagrams.
    • Describing visual content.

  2. Audio Processing

    • Transcription and summarization of audio clips.
    • Voice command interpretation.

  3. Code Understanding

    • Explaining code snippets.
    • Generating documentation from code.

  4. Cross-modal Summarization

    • Combining insights from text, images, and other data.
    • Generating reports from multi-modal sources.

When to Use Multi-modal Prompts​

Multi-modal prompting is ideal when:

  • The task requires understanding or generating information across different data types.
  • Visual, auditory, or code context is essential for accurate responses.
  • You want richer, more informative outputs.

When to Consider Alternatives​

Consider other techniques when:

  • The task can be accomplished with text alone.
  • The model or system does not support multi-modal input.
  • Simpler, single-modality prompts are sufficient.

Tips for Optimization​

  1. Use Descriptive Labels

    • Clearly mark each modality in the prompt.
    • Provide alternative text for images or audio if needed.

  2. Iterative Testing

    • Experiment with different combinations of modalities.
    • Refine prompts based on model performance.

  3. Simplify When Possible

    • Limit the number of modalities per prompt.
    • Focus on the most relevant data types for the task.

  4. Validate Outputs

    • Check that responses address all modalities.
    • Revise prompts if the model misses or misinterprets data.